KEYWORDS: Data modeling, Unmanned aerial vehicles, Electroencephalography, Data acquisition, Cross validation, Control systems, Support vector machines, Education and training, Cognitive modeling, Calibration
This article designs a cognitive data collection paradigm that integrates PVT (Psychomotor Vigilance Task) with cluster missions to activate operator mental fatigue states and accurately collect sample data. Based on the sample data, a support vector machine (SVM) based classification model for the mental fatigue states of cluster drone operators is trained and constructed. Using this model, a cluster drone operation duration determination model is developed by setting criteria for extreme fatigue states and calibrating model thresholds. Through 100 rounds of 5-fold cross-validation on the dataset, the results indicate an accuracy rate of 95.5% for determining the mental fatigue states of cluster drones.
KEYWORDS: Electromagnetic coupling, Brain, Electroencephalography, Electrodes, Interference (communication), Head, Industrial applications, Electrical conductivity, Analog to digital converters, 3D modeling
As an important hardware component in the process of EEG recognition, the brain information perception system undertakes the functions of accurate EEG signal acquisition, amplification, conversion, transmission and recognition. As the EEG signal shows the characteristics of weak in amplitude, low SNR, low frequency range and strong randomness, it can be easily affected by electromagnetic interference in the environment. Thus leads to a decrease in accuracy. Therefore, the interference sources that interfere with the brain information perception process are sorted out, the simulation is carried out for different objective factors, in order to explore the influence of electromagnetic interference in the environment on the brain information perception system. Finally, we provide reference and ideas for the interference of the brain information perception system.
The extreme attention state is one of the cognitive states and it is extremely important for cluster operators due to the diversity and complexity of tasks. However, existing research tends to be more theoretical and there is relatively little research on extreme attention states. Therefore, we combine theoretical research with practical cluster drone control task to design our experimental paradigm. We used machine learning method to build classifiers and all of these classifiers achieved good results, which validates the rationality of our method. Finally, we choose the support vector machine (SVM) as our classifier due to the excellent result.
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